Current research on intergenerational mobility (IGM) is informed by "statistical" approaches based on log-level regressions, whose "economic" interpretations remain largely unknown. We reveal the subjective value-judgments in them: they are represented by weighted-sums (or aggregators) over heterogeneous groups, with…

The trace plot is seldom used in meta-analysis, yet it is a very informative plot. In this article, we define and illustrate what the trace plot is, and discuss why it is important. The Bayesian version of the plot combines the posterior density of [tau], the between-study standard deviation, and the shrunken estimates of the study effects as a…

Regression discontinuity (RD) designs are increasingly used for causal evaluations. For example, if a student's need for a literacy intervention is determined by a low score on a past performance indicator and that intervention is provided to all students who fall below a cutoff on that indicator, an RD study can determine the intervention's main…

School start regulations allocate children born immediately either side of a given date to different life paths: those slightly older starting school a full year earlier. School effectiveness literature exploits this to estimate causal effects described as 'the absolute effect of schooling' or 'the effect of an additional year's schooling', using…

Binary logistic regression is one of the most widely used statistical tools. The method uses odds, log odds, and odds ratios, which are difficult to understand and interpret. Understanding of logistic regression tends to fall down in one of three ways: (1) Many students and researchers come to believe that an odds ratio translates directly into…

Regression discontinuity (RD) designs are increasingly used for causal evaluations. However, the literature contains little guidance for conducting a moderation analysis within an RDD context. The current article focuses on moderation with a single binary variable. A simulation study compares: (1) different bandwidth selectors and (2) local…

Many large-scale assessments model proficiency with a latent regression on contextual variables. Item-response data are used to estimate the parameters of the latent variable model and are used in conjunction with the contextual data to generate plausible values of individuals' proficiency attributes. These models typically incorporate numerous…

Background/Context: In meta-analyses examining educational interventions, researchers seek to understand the distribution of intervention impacts, in order to draw generalizations about what works, for whom, and under what conditions. One common way to examine equity implications in such reviews is through moderator analysis, which involves…

Bayesian estimations of complex regression models with high-dimensional parameter spaces require advanced priors, capable of addressing both sparsity and multicollinearity in the data. The Dirichlet-horseshoe, a new prior distribution that combines and expands on the concepts of the regularized horseshoe and the Dirichlet-Laplace priors, is a…

Recurrent event analyses have found a wide range of applications in biomedicine, public health, and engineering, among others, where study subjects may experience a sequence of event of interest during follow-up. The R package reReg offers a comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events,…

Fuzzy regression-discontinuity evaluations of college remediation often find negative and null estimates of local average treatments effects (LATEs), but with substantial heterogeneity. We find that a remedial quantitative skills course at Wellesley College has a modestly positive LATE on participation in mathematically intensive fields of…

Higher education institutions have consistently strived to provide students with top-notch education. To achieve better outcomes, machine learning (ML) algorithms greatly simplify the prediction process. ML can be utilized by academicians to obtain insight into student data and mine data for forecasting the performance. In this paper, the authors…

Prior research has suggested that clustered regression discontinuity designs are a formidable alternative to cluster randomized designs because they provide targeted treatment assignment while maintaining a high-quality basis for inferences on local treatment effects. However, methods for the design and analysis of clustered regression…

Including a large number of predictors in the imputation model underlying a multiple imputation (MI) procedure is one of the most challenging tasks imputers face. A variety of high-dimensional MI techniques can help, but there has been limited research on their relative performance. In this study, we investigated a wide range of extant…

Background/Context: In meta-analysis examining educational intervention, characterizing heterogeneity and exploring the sources of variation in synthesized effects have become increasingly prominent areas of interest. When combining results from a collection of studies, statistical dependency among their effects size estimates will arise when a…